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Uncertainty measurement for heterogeneous data: an application in attribute reduction

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Abstract

In the era of big data, multimedia, hyper-media and social networks are emerging, and the amount of information is growing rapidly. When people participate in the process of massive data processing, they will encounter data with different structures, so data has heterogeneity. How to acquire hidden and valuable knowledge from heterogeneous data and measure its uncertainty is an important problem in artificial intelligence. This paper investigates uncertainty measurement for heterogeneous data and gives its application in attribute reduction. The concept of a heterogeneous information system (HIS) is first proposed. Then, an equivalence relation on the object set is constructed. Next, uncertainty measurement for a HIS is investigated, a numerical experiment is given, and dispersion analysis, correlation analysis, and Friedman test and Bonferroni–Dunn test in statistics are conducted. Finally, as an application of the proposed measures, attribute reduction in a HIS is studied, and the corresponding algorithms and their analysis are proposed.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which have helped immensely in improving the quality of the paper. This work is supported by National Natural Science Foundation of China (11971420), Special Scientific Research Project of Young Innovative Talents in Guangxi (2019AC20052), Natural Science Foundation of Guangxi (2019JJA110036, AD19245102, 2018GXNSFDA294003, 2018GXNSFDA294134), Guangxi Science and Technology Program(2017AD23056),Key Laboratory of Software Engineering in Guangxi University for Nationalities(2020-18XJSY-03), Guangxi Higher Education Institutions of China (Document No.[2019] 52), Guangxi Higher Education Reform Project (2020XJJGZD17), Research Project of Institute of Big Data in Yulin (YJKY03) and Engineering Project of Undergraduate Teaching Reform of Higher Education in Guangxi (2017JGA179).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Yan Song & Jiali He

  2. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Gangqiang Zhang & Ningxin Xie

  3. School of Mathematics and Physics, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Shimin Liao

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  1. Yan Song
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  2. Gangqiang Zhang
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  3. Jiali He
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  4. Shimin Liao
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  5. Ningxin Xie
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Correspondence to Gangqiang Zhang or Jiali He.

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Song, Y., Zhang, G., He, J. et al. Uncertainty measurement for heterogeneous data: an application in attribute reduction. Artif Intell Rev 55, 991–1027 (2022). https://doi.org/10.1007/s10462-021-09978-y

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